LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wilson, William O.; Feyereisl, Jan; Aickelin, Uwe (2010)
Languages: English
Types: Unknown
Subjects: Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Computer Science - Cryptography and Security
The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs which repeat within time series data. The power of the algorithm is derived from its use of a small number of parameters with minimal assumptions. The algorithm searches from a completely neutral perspective that is independent of the data being analysed, and the underlying motifs. In this paper the motif tracking algorithm is applied to the search for patterns within sequences of low level system calls between the Linux kernel and the operating system's user space. The MTA is able to compress data found in large system call data sets to a limited number of motifs which summarise that data. The motifs provide a resource from which a profile of executed processes can be built. The potential for these profiles and new implications for security research are highlighted. A higher level call system language for measuring similarity between patterns of such calls is also suggested.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Nunn, I., White, T.: The application of antigenic search techniques to time series forecasting. GECCO (June 2005) 353-360
    • 2. Wilson, W.O., Birkin, P., Aickelin, U.: Motif detection inspired by immune memory. In: Proceedings of the 6th International Conference on Artificial Immune Systems (ICARIS 2007). Lecture Notes in Computer Science, Santos, Brazil (2007)
    • 3. de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3) (2002) 239-251
    • 4. Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In the 2nd workshop on temporal data mining, at the 8th ACM SIGKDD international conference on knowledge discovery and data mining (July, 2002)
    • 5. Guan, X., Uberbacher, E.C.: A fast look up algorithm for detecting repetitive dna sequences. Pacific symposium on biocomputing, Hawaii IEEE Tran. Control Systems Tech. (December 1996)
    • 6. Keogh, E., Smyth, P.: A probabilistic approach to fast pattern matching in time series databases. In proceedings of the third international conference of knowledge discovery and data mining (1997) 20-24
    • 7. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In proceedings of the SIGMOD conference (1994) 419-429
    • 8. Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. SIGKDD (August, 2003)
    • 9. Lin, J., Keogh, E., Lonardi, S.: Visualizing and discovering non trivial patterns in large time series databases. Information visualization 4, issue 2 (2005) 61-82
    • 10. Tanaka, Y., Uehara, K.: Discover motifs in multi-dimensional time series using the principal component analysis and the mdl principle. 3rd international conference on machine learning and data mining in pattern recognition Leipzig, Germany (2003) 252-265
    • 11. Fu, T.C., Chung, F.L., Ng, V., Luk, R.: Pattern discovery from stock market time series using self organizing maps. Workshop notes of KDD2001 workshop on temporal data mining. San francisco, CA (2001) 27-37
    • 12. Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A.: A sense of self for UNIX processes. In: IEEE Symposium on Security and Privacy, Oakland, CA, IEEE Computer Society Press (1996) 120-128
    • 13. Sekar, R., Bowen, T., Segal, M.: On preventing intrusions by process behavior monitoring. In: Proceedings of the Workshop on Intrusion Detection and Network Monitoring, Berkeley, CA, USENIX Association (April 9-12 1999) 29-40
    • 14. Warrender, C., Forrest, S., Pearlmutter, B.: Detecting intrusions using system calls: Alternative data models. In: Proceedings of the 1999 Conference on Security and Privacy (S&P-99), Los Alamitos, CA, IEEE Press (May 9-12 1999) 133-145
    • 15. Tandon, G., Chan, P., Mitra, D.: Morpheus: motif oriented representations to purge hostile events from unlabeled sequences. In: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, NY, USA (2004) 16-25
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article